{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Introduction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook explores many of the ideas described by Marcos Lopez de Prado in his book \"Advances in Financial Machine Learning.\" I made use of many of the code snippets provided throughout the book as well as a library called mlfinlab, which has aggregated and expanded on much of the code in the book. I tried to give a brief primer on each concept being utilized from the book for my own reference and anyone interested."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lopez de Prado's book works primarily with tick data, which is very expensive to acquire for the most part, however, I was able to freely download Forex tick data from TrueFx.com.\n",
    "\n",
    "This notebook focuses strictly on tick data for the USD/JPY currency. USD/JPY tick data spanning from 2014-2018 was used for research, and the best performing strategy was backtested on tick data from 2011-2013.\n",
    "\n",
    "The data includes the timestamp of the tick as well as the bid and ask price. Because Forex does not have a central exchange, volume data for each tick is not a part of the data set. As a result of this, data was sampled in tick bars, every 2800 ticks. \n",
    "\n",
    "The tick bars function in mlfinlab was used to sample the data. The function returns a DataFrame with open, high, low, and close prices for each tick. The motivation for using tick bars opposed to time bars is that tick bars exhibit a distribution of returns that is much closer to normal opposed to time bars. This is exhibited in the graph below:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Tick bars also more realistically represent the market as trading frequency varies throughout the day. \n",
    "\n",
    "One of the drawbacks to sampling tick bars, however, is that sampling frequency is often very inconsistent throughout time. This is shown below:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The sampling frequency when bars are sampled as a function of the volume traded or the dollar amount traded is much more consistent throughout time, however, both require volume data which was not available in this dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2837,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "import seaborn as sns\n",
    "\n",
    "import scipy\n",
    "from scipy import stats\n",
    "\n",
    "import mlfinlab as mlf\n",
    "from mlfinlab.util import multiprocess\n",
    "\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.preprocessing import LabelEncoder, StandardScaler\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.model_selection._split import _BaseKFold\n",
    "\n",
    "from statsmodels.tsa.stattools import adfuller\n",
    "from statsmodels.tsa.stattools import kpss\n",
    "\n",
    "import math\n",
    "\n",
    "import empyrical\n",
    "\n",
    "import talib\n",
    "\n",
    "import multiprocessing as mp\n",
    "from multiprocessing import cpu_count\n",
    "\n",
    "from sklearn.utils import shuffle\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.ensemble import BaggingClassifier\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import roc_curve, auc\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.decomposition import PCA\n",
    "\n",
    "import keras\n",
    "from keras.wrappers.scikit_learn import KerasClassifier\n",
    "from keras.layers import Dense\n",
    "from keras.layers import Dropout\n",
    "from keras import Sequential\n",
    "from keras import optimizers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Reading in dataframe with precomputed features\n",
    "\n",
    "tick_bars = pd.read_csv('FILE_PATH',index_col=0, parse_dates=True)\n",
    "ask = tick_bars['ask'].copy()\n",
    "bid = tick_bars['bid'].copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feature Engineering"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the purposes of feature engineering as well as labeling, the mid-price of each tick was used, however, when it came to actually calculating the returns based on the predictions of the model, the bid and ask prices were used. \n",
    "\n",
    "The initial features included various technical analysis and signal processing features calculated at different time periods. The majority of these features were derived using the library ta-lib. The full list of initial features can be found [here](https://github.com/JackBrady/Financial-Machine-Learning-Research/blob/master/Initial_Feature_List). The inspiration for many of these features comes from their ubiquity in academic research pertaining to machine learning methods in the Forex market."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Additionally, 7 fractionally differentiated features were generated. Fractionally differentiated features are a concept presented by Dr. Lopez de Prado. The idea is that when a price series is differentiated to calculate log returns, we lose all memory of the underlying series in an effort to achieve stationarity. Prices, in contrast to returns have memory, however, are not stationary. Dr. Lopez de Prado proposes a method to difference a price series to achieve stationarity, without fully differencing the series and thereby losing all memory. The motivation being that conserving memory will yield more predictive power. This fractionally differentiated series can then be used as a feature. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2906,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>adfStat</th>\n",
       "      <th>pVal</th>\n",
       "      <th>lags</th>\n",
       "      <th>nObs</th>\n",
       "      <th>95% conf</th>\n",
       "      <th>corr</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.0</th>\n",
       "      <td>-2.106910</td>\n",
       "      <td>2.417452e-01</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1318.0</td>\n",
       "      <td>-2.863735</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.1</th>\n",
       "      <td>-2.393264</td>\n",
       "      <td>1.436467e-01</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1313.0</td>\n",
       "      <td>-2.863744</td>\n",
       "      <td>0.999334</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.2</th>\n",
       "      <td>-2.909197</td>\n",
       "      <td>4.428895e-02</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1310.0</td>\n",
       "      <td>-2.863749</td>\n",
       "      <td>0.994632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.3</th>\n",
       "      <td>-3.897853</td>\n",
       "      <td>2.050704e-03</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1310.0</td>\n",
       "      <td>-2.863749</td>\n",
       "      <td>0.980985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.4</th>\n",
       "      <td>-5.199077</td>\n",
       "      <td>8.830312e-06</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1310.0</td>\n",
       "      <td>-2.863749</td>\n",
       "      <td>0.955427</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.5</th>\n",
       "      <td>-7.049648</td>\n",
       "      <td>5.569259e-10</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1311.0</td>\n",
       "      <td>-2.863747</td>\n",
       "      <td>0.907075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.6</th>\n",
       "      <td>-9.608250</td>\n",
       "      <td>1.841198e-16</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1312.0</td>\n",
       "      <td>-2.863745</td>\n",
       "      <td>0.822383</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.7</th>\n",
       "      <td>-12.281292</td>\n",
       "      <td>8.212373e-23</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1314.0</td>\n",
       "      <td>-2.863742</td>\n",
       "      <td>0.728096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.8</th>\n",
       "      <td>-16.363358</td>\n",
       "      <td>2.856350e-29</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1315.0</td>\n",
       "      <td>-2.863740</td>\n",
       "      <td>0.557746</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.9</th>\n",
       "      <td>-20.075835</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1315.0</td>\n",
       "      <td>-2.863740</td>\n",
       "      <td>0.407113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <td>-24.616264</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1317.0</td>\n",
       "      <td>-2.863737</td>\n",
       "      <td>0.040225</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotMinFFD(tick_bars,'close')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we can see that differencing our USD/JPY price series by 0.2 achieves stationarity, p < 0.05, while still having a very strong correlation to the original price series. The function for plotting the minimum amount of differencing needed to achieve stationarity as well as the functions to actual calculate the fractionally differentiated features are provided by Dr. Lopez de Prado and can be found [here](https://github.com/JackBrady/Financial-Machine-Learning-Research/blob/master/Code/Fractionally_Differentiated_Features.py)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "While the initial feature set consisted of 57 features, our final feature set contained 31 features that were selected using two feature selection methods as well as by testing various subsets of features. These feature selection methods are discussed below."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feature Selection"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Two feature selection methods were used to generate the final set of features. The first method was mean decrease impurity (MDI), a tree based method, which calculates how much each individual feature decreases the overall impurity. The functions to calculate and plot feature importance based on MDI were provided by Dr. Lopez de Prado and were used to generate the figure below. This plot shows the feature importance for the initial set of 57 features. The code for each function can be found [here](https://github.com/JackBrady/Financial-Machine-Learning-Research/blob/master/Code/Feature_Importance.py).\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The second method used is an input perturbation feature ranking algorithm demonstrated by Dr. Jeff Heaton in the following video: [link](https://www.youtube.com/watch?v=RVIGVkj5aXo&t=1105s)\n",
    "\n",
    "The idea behind this method is that a feature's column is shuffled, and the accuracy of the model is then re-evaluated with the shuffled column. The significance of decrease in model accuracy with the shuffled column determines the respective feature's importance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 322,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.939444276368361"
      ]
     },
     "execution_count": 322,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Correlation between feature importance methods\n",
    "\n",
    "pert_rank['importance'].corr(mdi_p['mean'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The list of the final 31 features used can be found [here](https://github.com/JackBrady/Financial-Machine-Learning-Research/blob/master/Final_Feature_List)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Downsampling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The tick bars sampled previously were downsampled using the CUSUM filter provided by Dr. Lopez de Prado, which, as stated by Dr. Lopez de Prado,  is \"designed to detect a shift in the mean value of a measured quantity away from a target value.”  The intuition is that we want to make a prediction on an observation after a certain threshold is reached opposed to just predicting at a random point in time. In our case a bar was sampled if the cumulative sums of the price differences in either direction surpassed 1/10 the mean daily volatility. The CUSUM filter supplied by mlfinlab was utilized below:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2844,
   "metadata": {},
   "outputs": [],
   "source": [
    "closing = tick_bars['close']\n",
    "volatility = get_daily_volatility(closing)\n",
    "times = mlf.filters.cusum_filter(closing, volatility.mean()*.1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Additionally, the get_daily_volatility function was provided by Dr. Lopez de Prado, and is available [here](https://github.com/JackBrady/Financial-Machine-Learning-Research/blob/master/Code/Utility_Functions.py)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Labeling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Instead of labeling observations based on the sign of their returns after a given amount of time, observations are given a label based on whether or not they reached reached a return that is based on their respective volatilities. This allows for observations with more risk to have a higher expected return and vice versa. If an observations does not reach its expected return in a predetermined amount of time, it is given a label of 0, representing a return that is too low for us to make a bet on. If it reaches its return in the positive direction (upper barrier), a label of 1 is given, representing a long position. If it reaches its return in the negative direction (lower barrier), a label of -1 is given, representing a short position. \n",
    "\n",
    "This concept was presented by Dr. Lopez de Prado, and is known as the triple-barrier method, as we have a vertical (time) barrier and a horizontal barrier for long and short positions."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As stated previously, the mid-price of each tick was used for labeling observations."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "mlfinlab was utilized for the following labeling functions. Additionally, Dr. Lopez de Prado provides a multiprocessing engine to speed up computation, which was made us of in the get_events function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1 day was the amount of time set for the vertical barrier\n",
    "# The minimum return for an observation to be considered was set to 0.004\n",
    "# The upper and lower barrier were not scaled\n",
    "\n",
    "vertical_barriers = mlf.labeling.add_vertical_barrier(times, closing, num_days=1)\n",
    "pt_sl = [1,1]\n",
    "min_ret = 0.004\n",
    "threads = cpu_count()-1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "triple_barrier_events = mlf.labeling.get_events(closing,\n",
    "                                               times,\n",
    "                                               pt_sl,\n",
    "                                               volatility,\n",
    "                                               min_ret,\n",
    "                                               threads,\n",
    "                                               vertical_barriers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "labels_one = mlf.labeling.get_bins(triple_barrier_events, closing)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       " 0    6672\n",
       " 1    5290\n",
       "-1    5056\n",
       "Name: bin, dtype: int64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels_one['bin'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model Architecture/Meta-Labeling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The model architecture used in this project is based on the concept of meta-labeling formulated by Dr. Lopez de Prado. \n",
    "\n",
    "The idea behind meta-labeling is that we have a primary and a secondary binary classifier. The primary classifier predicts the side of the bet (-1,1), while the secondary classifier predicts whether or not we want to take the bet (0,1). It has been shown previously how labels are derived for the primary classifier, however, the labels derived for the secondary classifier, i.e. meta-labels, are obtained using a modified version of the get_events function which takes in the side predicted by the primary classifier. \n",
    "\n",
    "For example, if our primary classifier predicts a 1 for an observation, but the actual label is a -1, then this observation will receive a meta-label of 0, and the secondary classifier will be trained to predict a 0. Additionally, it can be seen previously that many observations did not reach their target return and thus received a label of 0. If this is the case, these observations will also receive a meta-label of 0. A meta-label of 1 is only given when the primary classifier's prediction is accurate."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The motivation for this technique is that the secondary classifier can learn from the error of the primary classifier, allowing it to act as a filter for bets."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Training Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The observations that initially received a label of 0, i.e. they did not reach their target return in time, were not used to train the primary model, however, they were used to train the secondary model. The motivation for this is that there is no point in training the primary model to recognize observations that will ideally receive a prediction of 0 from the secondary model, regardless of the primary model's prediction. The full training process then goes as follows:\n",
    "\n",
    "The primary model is trained on observations receiving a label of -1 or 1. \n",
    "Then, the observations receiving a label of 0 are aggregated with the training data. \n",
    "This full set of observations is then passed through the trained primary model and meta-labels are derived on these predictions. \n",
    "The secondary model is then trained using the same features as the primary model with the predicted side of the primary model being an additional feature. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following code prepares our data for training:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Full DataFrame including all observations regardless of label\n",
    "\n",
    "full_df = pd.DataFrame(tick_bars.loc[labels_one['bin'].index], index=labels_one['bin'].index)\n",
    "full_df.drop(columns=['open','high','close','low','bid','ask'],inplace=True)\n",
    "full_df['labels'] = labels_one['bin'].copy()\n",
    "y = full_df['labels'].copy()\n",
    "full_df.drop(columns=['labels'],inplace=True)\n",
    "x = full_df.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_full, X_test_full, y_train_full, y_test_full = train_test_split(x, y, test_size=0.20,\n",
    "                                                                        shuffle=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Start and end times for an observation\n",
    "\n",
    "t1 = triple_barrier_events['t1'].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#training observations purged. This will be discussed in the next section\n",
    "\n",
    "train_i = t1.loc[X_train_full.index]\n",
    "test_i = t1.loc[X_test_full.index]\n",
    "train_times = getTrainTimes(train_i,test_i)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It should be noted that while the test data for the primary model does not contain any observations with a label of 0, the test data being used for the secondary model is an aggregation of all test data regardless of label. Additionally, if the training set was transformed in any way, the same scaling was applied to the testing set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_full = X_train_full.loc[train_times.index]\n",
    "y_train_full = y_train_full.loc[train_times.index]\n",
    "\n",
    "# Getting dataframe for observations with labels -1 and 1\n",
    "\n",
    "X_train = X_train_full[y_train_full != 0].copy()\n",
    "\n",
    "# Getting a dataframe for observations with label 0\n",
    "\n",
    "X_train_addit = X_train_full[y_train_full == 0].copy()\n",
    "\n",
    "y_train = y_train_full.loc[X_train.index].copy()\n",
    "\n",
    "X_test = X_test_full[y_test_full != 0].copy()\n",
    "\n",
    "y_test = y_test_full.loc[X_test.index].copy()\n",
    "\n",
    "X = pd.concat([X_train,X_test])\n",
    "y = pd.concat([y_train,y_test])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Standardizing the data\n",
    "\n",
    "scaler = StandardScaler()\n",
    "new_training = scaler.fit_transform(X_train)\n",
    "\n",
    "new_testing = scaler.transform(X_test)\n",
    "\n",
    "new_testing_full = scaler.transform(X_test_full)\n",
    "\n",
    "X_train_stand = pd.DataFrame(new_training,index=X_train.index)\n",
    "X_test_stand = pd.DataFrame(new_testing,index=X_test.index)\n",
    "X_test_stand_full = pd.DataFrame(new_testing_full,index=X_test_full.index)\n",
    "\n",
    "scaler = StandardScaler()\n",
    "full_stand = scaler.fit_transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''\n",
    "This standardizes the observations with a label of 0 separately from the primary model's training set. \n",
    "These observations will not be used for training the primary model.\n",
    "They will be aggregated with the original training set post-training and the full output will be fed to the\n",
    "secondary model.\n",
    "'''\n",
    "\n",
    "scaler_addit = StandardScaler()\n",
    "new_training_addit = scaler_addit.fit_transform(X_train_addit)\n",
    "X_train_addit_stand = pd.DataFrame(new_training_addit,index=X_train_addit.index)\n",
    "X_train_stand_full = pd.concat([X_train_stand,X_train_addit_stand])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Sample Weights"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Observations are weighted as a function of their respective uniqueness. An observation is deemed completely unique if the time interval used to generate its label has no overlap with the time interval used to generate the label of another observation. The more overlap an observation has and the longer those overlaps last, the less unique the observation is. Additionally, a linear time-decay is applied to the sample weights, giving older observations less importance. This method for calculating sample weights was demonstrated by Dr. Lopez de Prado as well as the method for calculating the uniqueness of an observation. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "The following function calls many of the functions provided by Dr. Lopez de Prado and returns sample weights \n",
    "as well as the average uniqueness across all observations. It also makes use of the multiprocessing engine mentioned previously. Code for these functions can be found: [here](https://github.com/JackBrady/Financial-Machine-Learning-Research/blob/master/Code/Sample_Weights.py) \n",
    "                            \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "training_weights = get_weights_and_avgu(closing,X_train,threads,t1)[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "avgu,sample_weights = get_weights_and_avgu(closing,X,threads,t1) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the previous section, observations were removed from the training set if the time interval used for generating their labels had any overlap with the observations in the testing set. This technique, proposed by Dr. Lopez de Prado, is known as \"purging\" and aims at removing any sort of leakage between the train and test set. Code provided by Dr. Lopez de Prado for purging the training set can be found: [here](https://github.com/JackBrady/Financial-Machine-Learning-Research/blob/master/Code/Cross_Validation.py)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model Selection"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Three different models were used for the primary classifier in this research:\n",
    "<br>\n",
    "The first model was a bagged decision tree.\n",
    "<br>\n",
    "The second was a bagged SVM.\n",
    "<br>\n",
    "The third was a MLP."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The reason I used bagged decision trees opposed to sklearn's random forest classifier was due to severe overfitting issues I was having with the traditional random forest model. This is very likely a result of observational redundancy in our training set, i.e. many overlapping outcomes. To mitigate the issue of redundancy, Dr. Lopez de Prado recommended using a bagging classifier and setting the max_samples parameter to the average uniqueness of our observations, which helps prevent the creation of many redundant trees. While the bagging classifier supports the max_samples parameter, it is not supported in sklearn's random forest classifier."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The initial plan was to use these three models as both primary and secondary classifiers and to test every combination of models yielding 9 potential combinations. As secondary classifiers, however, the bagged SVM and MLP were severely overpredicting the majority class even after adding class weights, upsampling the minority class, and attempting to tune hyperparameters using f1 as the scoring metric. Because of these issues, the bagged decision tree was the only model used as a secondary classifier."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feature Extraction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "PCA was the only feature extraction technique used and showed improvement in accuracy for the primary bagged decision tree classifier. A function to calculate the the minimum number of orthogonal features which account for 95% of the variance of the standardized data was provided by Dr. Lopez de Prado. This function was utilized to calculate the number of principal components to be used, which was found to be 7. Code for this function can be found [here](https://github.com/JackBrady/Financial-Machine-Learning-Research/blob/master/Code/Feature_Extraction.py)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2862,
   "metadata": {},
   "outputs": [],
   "source": [
    "pc = orthoFeats(X_train)\n",
    "num_feat = pc.shape[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2885,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = StandardScaler()\n",
    "new_training = scaler.fit_transform(X_train)\n",
    "\n",
    "new_testing = scaler.transform(X_test)\n",
    "new_testing_full = scaler.transform(X_test_full)\n",
    "\n",
    "pca = PCA(n_components=num_feat)\n",
    "X_train_pca = pca.fit_transform(new_training)\n",
    "X_test_pca = pca.transform(new_testing)\n",
    "X_test_full_pca = pca.transform(new_testing_full)\n",
    "\n",
    "pca_addit = PCA(n_components=num_feat)\n",
    "X_train_addit_pca = pca_addit.fit_transform(X_train_addit)\n",
    "\n",
    "X_train_pca_df = pd.DataFrame(X_train_pca,index=X_train.index)\n",
    "X_train_addit_pca_df = pd.DataFrame(X_train_addit_pca,index=X_train_addit.index)\n",
    "\n",
    "X_train_full_pca = pd.concat([X_train_pca_df,X_train_addit_pca_df])\n",
    "X_test_full_pca_df = pd.DataFrame(X_test_full_pca,index=X_test_full.index)\n",
    "\n",
    "scaler_full = StandardScaler()\n",
    "full_scaled = scaler.fit_transform(X)\n",
    "full = PCA(n_components=num_feat)\n",
    "full_pca = full.fit_transform(full_scaled)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Primary Model Training/Hyperparameter Tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2860,
   "metadata": {},
   "outputs": [],
   "source": [
    "dt=DecisionTreeClassifier(criterion='entropy',max_features='auto',\n",
    "                          class_weight='balanced',min_weight_fraction_leaf=0.05,random_state=20)\n",
    "\n",
    "bagged_dt=BaggingClassifier(base_estimator=dt,n_estimators=1000,max_samples=avgu,\n",
    "                            max_features=1.,random_state=20)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2864,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "BaggingClassifier(base_estimator=DecisionTreeClassifier(class_weight='balanced',\n",
       "                                                        criterion='entropy',\n",
       "                                                        max_depth=None,\n",
       "                                                        max_features='auto',\n",
       "                                                        max_leaf_nodes=None,\n",
       "                                                        min_impurity_decrease=0.0,\n",
       "                                                        min_impurity_split=None,\n",
       "                                                        min_samples_leaf=1,\n",
       "                                                        min_samples_split=2,\n",
       "                                                        min_weight_fraction_leaf=0.05,\n",
       "                                                        presort=False,\n",
       "                                                        random_state=20,\n",
       "                                                        splitter='best'),\n",
       "                  bootstrap=True, bootstrap_features=False, max_features=1.0,\n",
       "                  max_samples=0.07594124165785086, n_estimators=1000,\n",
       "                  n_jobs=None, oob_score=False, random_state=20, verbose=0,\n",
       "                  warm_start=False)"
      ]
     },
     "execution_count": 2864,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bagged_dt.fit(X_train_pca,y_train,sample_weight=training_weights)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The default parameters for C and gamma were used."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "svc = SVC(probability=True,gamma='auto',random_state=20)\n",
    "\n",
    "SVC_bagged=BaggingClassifier(base_estimator=svc,n_estimators=1000,max_samples=avgu,\n",
    "                                   random_state=20,max_features=1.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "BaggingClassifier(base_estimator=SVC(C=1.0, cache_size=200, class_weight=None,\n",
       "                                     coef0=0.0, decision_function_shape='ovr',\n",
       "                                     degree=3, gamma='auto', kernel='rbf',\n",
       "                                     max_iter=-1, probability=True,\n",
       "                                     random_state=20, shrinking=True, tol=0.001,\n",
       "                                     verbose=False),\n",
       "                  bootstrap=True, bootstrap_features=False, max_features=1.0,\n",
       "                  max_samples=0.07594124165785086, n_estimators=1000,\n",
       "                  n_jobs=None, oob_score=False, random_state=20, verbose=0,\n",
       "                  warm_start=False)"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SVC_bagged.fit(X_train_stand,y_train,sample_weight=training_weights)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2213,
   "metadata": {},
   "outputs": [],
   "source": [
    "def build_mlp(input_size,lr,classes):\n",
    "    from numpy.random import seed\n",
    "    seed(0)\n",
    "    from tensorflow import set_random_seed\n",
    "    set_random_seed(0)\n",
    "    \n",
    "    model = Sequential()\n",
    "    model.add(Dense(10,input_dim=input_size,activation='relu'))\n",
    "    model.add(Dropout(0.2))\n",
    "    model.add(Dense(10))\n",
    "    model.add(Dropout(0.2))\n",
    "    model.add(Dense(classes,activation='sigmoid'))\n",
    "    optimizer = optimizers.adam(lr)\n",
    "    model.compile(loss='binary_crossentropy',optimizer=optimizer)\n",
    "    return model\n",
    "\n",
    "MLP = KerasClassifier(build_fn=build_mlp,input_size=X_train_stand.shape[1],classes=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Hyperparameter Tuning (Grid Search)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As stated previously, observations from the training set that overlap the testing set must be purged. This is also necessary in k-fold cross validation, where we have potential overlap on both sides of the test set. The purged k-fold class, provided by Dr. Lopez Prado [link](https://github.com/JackBrady/Financial-Machine-Learning-Research/blob/master/Code/Cross_Validation.py), is implemented in the hyperparameter tuning function below. This function makes use of sklearn's GridSearchCV, however, instead of traditional k-fold cross validation, purged k-fold cross validation is used.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "param_grid={'lr':[0.00001,0.0001,0.001,0.01,.1]}\n",
    "\n",
    "t1cv = t1.loc[X_train_stand.index].copy()\n",
    "tuned_mlp = clfHyperFitnn(X_train_stand,y_train,t1cv,mlp,param_grid,training_weights,cv=5)\n",
    "tuned_mlp = tuned_mlp.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2074,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'input_size': 31, 'classes': 2, 'epochs': 200, 'lr': 0.0001}"
      ]
     },
     "execution_count": 2074,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tuned_MLP.sk_params"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The hyperparameter tuning function used previously to tune the learning rate is a modified version of Dr. Lopez de Prado's and can be found [here](https://github.com/JackBrady/Financial-Machine-Learning-Research/blob/master/Code/Hyperparameter_Tuning.py)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Primary Model Results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The results from the test set for each primary classifier are aggregated in the DataFrame below, however, if one wishes to view the code and output for the classification reports, ROC curve, and CV score for each model, it is also available below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 340,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Accuracy</th>\n",
       "      <th>CV Score</th>\n",
       "      <th>AUC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Bagged DT</th>\n",
       "      <td>57%</td>\n",
       "      <td>55.51%</td>\n",
       "      <td>.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bagged SVM</th>\n",
       "      <td>53%</td>\n",
       "      <td>53.90%</td>\n",
       "      <td>.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ANN</th>\n",
       "      <td>59%</td>\n",
       "      <td>54.18%</td>\n",
       "      <td>.67</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Accuracy CV Score  AUC\n",
       "Bagged DT       57%   55.51%  .65\n",
       "Bagged SVM      53%   53.90%  .65\n",
       "ANN             59%   54.18%  .67"
      ]
     },
     "execution_count": 340,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "primary_res_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Primary Classifier: Bagged Decision Trees"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Training Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "          -1       0.61      0.57      0.59      3974\n",
      "           1       0.63      0.67      0.65      4415\n",
      "\n",
      "    accuracy                           0.62      8389\n",
      "   macro avg       0.62      0.62      0.62      8389\n",
      "weighted avg       0.62      0.62      0.62      8389\n",
      "\n"
     ]
    }
   ],
   "source": [
    "y_pred = bagged_dt.predict(X_train_pca)\n",
    "print(classification_report(y_train, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Test Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "          -1       0.66      0.47      0.55      1054\n",
      "           1       0.52      0.70      0.60       875\n",
      "\n",
      "    accuracy                           0.57      1929\n",
      "   macro avg       0.59      0.59      0.57      1929\n",
      "weighted avg       0.60      0.57      0.57      1929\n",
      "\n"
     ]
    }
   ],
   "source": [
    "y_pred2 = bagged_dt.predict(X_test_pca)\n",
    "print(classification_report(y_test, y_pred2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Mean CV Score on Full Data Set \n",
    "(Metric: Accuracy)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The cvScore function was provided by Dr. Lopez de Prado and also makes use of the purged k-fold class. Code for this function can be found [here](https://github.com/JackBrady/Financial-Machine-Learning-Research/blob/master/Code/Cross_Validation.py)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2019-07-09 19:06:38.891275 100.0% mpNumCoEvents done after 0.03 minutes. Remaining 0.0 minutes..\n",
      "2019-07-09 19:06:40.387380 100.0% mpSampleTW done after 0.02 minutes. Remaining 0.0 minutes..\n",
      "2019-07-09 19:06:42.476432 100.0% mpSampleW done after 0.03 minutes. Remaining 0.0 minutes..\n"
     ]
    }
   ],
   "source": [
    "y = y.loc[X.index].copy()\n",
    "sample_weights = get_weights_and_avgu(closing,X,threads,t1)[1]\n",
    "vert_barr = t1.loc[X.index].copy()\n",
    "full_pca_df = pd.DataFrame(full_pca,index=X.index)\n",
    "\n",
    "scores = cvScore(bagged_dt,full_pca_df,y,sample_weights,scoring='accuracy',\n",
    "                 t1=vert_barr,cv=10,pctEmbargo=0.01)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5551425632320195"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scores.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Primary Classifier: Bagged SVM"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Training Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "          -1       0.63      0.47      0.54      3974\n",
      "           1       0.61      0.75      0.68      4415\n",
      "\n",
      "    accuracy                           0.62      8389\n",
      "   macro avg       0.62      0.61      0.61      8389\n",
      "weighted avg       0.62      0.62      0.61      8389\n",
      "\n"
     ]
    }
   ],
   "source": [
    "y_pred = SVC_bagged.predict(X_train_stand)\n",
    "print(classification_report(y_train, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Test Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "          -1       0.77      0.20      0.31      1054\n",
      "           1       0.49      0.93      0.64       875\n",
      "\n",
      "    accuracy                           0.53      1929\n",
      "   macro avg       0.63      0.56      0.48      1929\n",
      "weighted avg       0.64      0.53      0.46      1929\n",
      "\n"
     ]
    }
   ],
   "source": [
    "y_pred = SVC_bagged.predict(X_test_stand)\n",
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Mean CV Score on Full Data Set \n",
    "(Metric: Accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "full_stand_df = pd.DataFrame(full_stand,index=X.index)\n",
    "\n",
    "scores = cvScore(SVC_bagged,full_stand_df,y,sample_weights,scoring='accuracy',\n",
    "                 t1=vert_barr,cv=10,pctEmbargo=0.01)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.53904931652846"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scores.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Primary Classifier: MLP"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Training Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 333,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "          -1       0.62      0.44      0.52      3974\n",
      "           1       0.60      0.76      0.67      4415\n",
      "\n",
      "    accuracy                           0.61      8389\n",
      "   macro avg       0.61      0.60      0.59      8389\n",
      "weighted avg       0.61      0.61      0.60      8389\n",
      "\n"
     ]
    }
   ],
   "source": [
    "y_pred = tuned_MLP.predict(X_train_stand)\n",
    "print(classification_report(y_train, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Test Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 334,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "          -1       0.74      0.38      0.50      1054\n",
      "           1       0.53      0.84      0.65       875\n",
      "\n",
      "    accuracy                           0.59      1929\n",
      "   macro avg       0.63      0.61      0.57      1929\n",
      "weighted avg       0.64      0.59      0.57      1929\n",
      "\n"
     ]
    }
   ],
   "source": [
    "y_pred = tuned_MLP.predict(X_test_stand)\n",
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Mean CV Score on Full Data Set \n",
    "(Metric: Accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "scores = cvScorenn(tuned_MLP,full_stand_df,y,sample_weights,200,scoring='accuracy',\n",
    "                   t1=vert_barr,cv=10,pctEmbargo=0.01)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 338,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5417619537833829"
      ]
     },
     "execution_count": 338,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scores.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Primary/Secondary Model Training Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The code for preparing data for the secondary model is shown below. The get_events function used previously is used once more, this time taking in the side predicted by the primary model in order to generate the meta-labels. Additionally, the prediction from the primary model is used as a feature for the secondary model. The following code is for the MLP as the primary classifier, however, the process is the same regardless of which model gave the predictions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 341,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_full = tuned_MLP.predict(X_train_stand_full)\n",
    "primary_l = y_pred_full.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 342,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_full_test = tuned_MLP.predict(X_test_stand_full)\n",
    "primary_l_test = y_pred_full_test.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 344,
   "metadata": {},
   "outputs": [],
   "source": [
    "side_train = pd.DataFrame(primary_l.copy(),index=X_train_stand_full.index)\n",
    "side_test = pd.DataFrame(primary_l_test.copy(),index=X_test_stand_full.index)\n",
    "side = pd.concat([side_train,side_test])\n",
    "side.sort_index(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 346,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/ipykernel_launcher.py:1: FutureWarning: \n",
      "Passing list-likes to .loc or [] with any missing label will raise\n",
      "KeyError in the future, you can use .reindex() as an alternative.\n",
      "\n",
      "See the documentation here:\n",
      "https://pandas.pydata.org/pandas-docs/stable/indexing.html#deprecate-loc-reindex-listlike\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "times = side.index\n",
    "vertical_barriers = vertical_barriers.loc[side.index]\n",
    "pt_sl = [1,1]\n",
    "min_ret = 0.004\n",
    "threads = cpu_count()-1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 347,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2019-07-09 23:13:31.015289 14.29% apply_pt_sl_on_t1 done after 0.15 minutes. Remaining 0.92 minutes.\r",
      "2019-07-09 23:13:31.024709 28.57% apply_pt_sl_on_t1 done after 0.15 minutes. Remaining 0.38 minutes.\r",
      "2019-07-09 23:13:31.050472 42.86% apply_pt_sl_on_t1 done after 0.15 minutes. Remaining 0.21 minutes.\r",
      "2019-07-09 23:13:31.082585 57.14% apply_pt_sl_on_t1 done after 0.15 minutes. Remaining 0.12 minutes.\r",
      "2019-07-09 23:13:31.109259 71.43% apply_pt_sl_on_t1 done after 0.16 minutes. Remaining 0.06 minutes.\r",
      "2019-07-09 23:13:31.120781 85.71% apply_pt_sl_on_t1 done after 0.16 minutes. Remaining 0.03 minutes.\r",
      "2019-07-09 23:13:31.133115 100.0% apply_pt_sl_on_t1 done after 0.16 minutes. Remaining 0.0 minutes.\n"
     ]
    }
   ],
   "source": [
    "triple_barrier_events = mlf.labeling.get_events(closing,\n",
    "                                               times,\n",
    "                                               pt_sl,\n",
    "                                               volatility,\n",
    "                                               min_ret,\n",
    "                                               threads,\n",
    "                                               vertical_barriers,\n",
    "                                               side[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 348,
   "metadata": {},
   "outputs": [],
   "source": [
    "labels = mlf.labeling.get_bins(triple_barrier_events, closing)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 349,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    10636\n",
       "1     6293\n",
       "Name: bin, dtype: int64"
      ]
     },
     "execution_count": 349,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels['bin'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 350,
   "metadata": {},
   "outputs": [],
   "source": [
    "t1 = triple_barrier_events['t1'].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 381,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_y_train = labels['bin'].loc[X_train_full_pca.index].copy()\n",
    "new_x_train = tick_bars.loc[X_train_full_pca.index].copy()\n",
    "\n",
    "new_y_test = labels['bin'].loc[X_test_full_pca.index].copy()\n",
    "new_x_test = tick_bars.loc[X_test_full_pca.index].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 382,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_x_train.dropna(inplace=True)\n",
    "new_x_train.drop(columns=['open','high','close','low','volume','bid','ask'],inplace=True)\n",
    "\n",
    "new_x_test.dropna(inplace=True)\n",
    "new_x_test.drop(columns=['open','high','close','low','volume','bid','ask'],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 383,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_x_train['predicted_side'] = side[0].loc[new_x_train.index].copy()\n",
    "new_x_test['predicted_side'] = side[0].loc[new_x_test.index].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 384,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_x_train.sort_index(inplace=True)\n",
    "new_y_train.sort_index(inplace=True)\n",
    "new_x_test.sort_index(inplace=True)\n",
    "new_y_test.sort_index(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 386,
   "metadata": {},
   "outputs": [],
   "source": [
    "full_new_x = pd.concat([new_x_train,new_x_test]).copy()\n",
    "full_new_y = pd.concat([new_y_train,new_y_test]).copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 385,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2019-07-09 23:27:07.237051 100.0% mpNumCoEvents done after 0.04 minutes. Remaining 0.0 minutes..\n",
      "2019-07-09 23:27:09.044871 100.0% mpSampleTW done after 0.03 minutes. Remaining 0.0 minutes..\n",
      "2019-07-09 23:27:11.590055 100.0% mpSampleW done after 0.04 minutes. Remaining 0.0 minutes..\n"
     ]
    }
   ],
   "source": [
    "avgmu,training_weights_meta = get_weights_and_avgu(closing,new_x_train,threads,t1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 387,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2019-07-09 23:27:16.134619 100.0% mpNumCoEvents done after 0.05 minutes. Remaining 0.0 minutes..\n",
      "2019-07-09 23:27:18.458274 100.0% mpSampleTW done after 0.04 minutes. Remaining 0.0 minutes..\n",
      "2019-07-09 23:27:21.616161 100.0% mpSampleW done after 0.05 minutes. Remaining 0.0 minutes..\n"
     ]
    }
   ],
   "source": [
    "sample_weights_meta = get_weights_and_avgu(closing,full_new_x,threads,t1)[1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Primary/Secondary Model Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 388,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "BaggingClassifier(base_estimator=DecisionTreeClassifier(class_weight='balanced',\n",
       "                                                        criterion='entropy',\n",
       "                                                        max_depth=None,\n",
       "                                                        max_features='auto',\n",
       "                                                        max_leaf_nodes=None,\n",
       "                                                        min_impurity_decrease=0.0,\n",
       "                                                        min_impurity_split=None,\n",
       "                                                        min_samples_leaf=1,\n",
       "                                                        min_samples_split=2,\n",
       "                                                        min_weight_fraction_leaf=0.05,\n",
       "                                                        presort=False,\n",
       "                                                        random_state=20,\n",
       "                                                        splitter='best'),\n",
       "                  bootstrap=True, bootstrap_features=False, max_features=1.0,\n",
       "                  max_samples=0.046828204993473545, n_estimators=1000,\n",
       "                  n_jobs=None, oob_score=False, random_state=20, verbose=0,\n",
       "                  warm_start=False)"
      ]
     },
     "execution_count": 388,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt_meta=DecisionTreeClassifier(criterion='entropy',max_features='auto',\n",
    "                               class_weight='balanced',min_weight_fraction_leaf=0.05,random_state=20)\n",
    "\n",
    "bagged_dt_meta=BaggingClassifier(base_estimator=dt_meta,n_estimators=1000,\n",
    "                                 max_samples=avgmu,max_features=1.,random_state=20)\n",
    "\n",
    "bagged_dt_meta.fit(new_x_train,new_y_train,sample_weight=training_weights_meta)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Primary/Secondary Model Results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The aggregated results from the test set for the three model combinations can be seen in the following DataFrame. The code and output for the results are also available below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 377,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Accuracy</th>\n",
       "      <th>CV Score</th>\n",
       "      <th>AUC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Bagged DT/Bagged DT</th>\n",
       "      <td>52%</td>\n",
       "      <td>54.78%</td>\n",
       "      <td>.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bagged SVM/Bagged DT</th>\n",
       "      <td>56%</td>\n",
       "      <td>55.78%</td>\n",
       "      <td>.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ANN/Bagged DT</th>\n",
       "      <td>53%</td>\n",
       "      <td>54.10%</td>\n",
       "      <td>.56</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Accuracy CV Score  AUC\n",
       "Bagged DT/Bagged DT       52%   54.78%  .55\n",
       "Bagged SVM/Bagged DT      56%   55.78%  .59\n",
       "ANN/Bagged DT             53%   54.10%  .56"
      ]
     },
     "execution_count": 377,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "secondary_res_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Performance and Risk Metrics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The metrics used to evaluate the success and risk of a model were: the Sharpe Ratio, cumulative returns, max drawdown, and percentage normalized profit (PNP). The function I wrote for PNP is inspired by the following paper: Baasheer and Fakhr [2011] [link](http://www.wseas.us/e-library/conferences/2011/Penang/ACRE/ACRE-05.pdf), and calculates the percentage of return we achieved out of the total return we could have achieved, had we predicted everything correctly in the test set. \n",
    "\n",
    "It should be stated that PNP is not a perfect metric for evaluating our model's profit versus the ideal profit because a bet that didn't reach its return should be classified as a 0, however, let's say that our secondary model misclassifies the bet as a 1 and the actual sign of the return was classified correctly by our primary model. This would present a situation where misclassification gained some profit that the ideal model could not have also gained. Regardless, PNP is still a useful metric in evaluating the profit of the secondary model given the primary model's predictions.\n",
    "\n",
    "The code for the PNP function can be found [here](https://github.com/JackBrady/Financial-Machine-Learning-Research/blob/master/Code/Backtest.py). Note that the returns calculated for this function are non-cumulative.\n",
    "\n",
    "Additionally, the function for calculating returns using the bid and ask prices can be found in the previous link.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The DataFrame for our performance and risk metrics can be seen below. Additionally, it must be noted that our returns do exhibit significant serial correlation. I partitioned the returns series into less correlated subsets, but did not notice any significant change in the Sharpe Ratio estimate when calculated on these subsets. It still must be noted, however, that the Sharpe Ratio will be inflated as a result of this serial correlation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 379,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PNP</th>\n",
       "      <th>Cumulative Returns</th>\n",
       "      <th>Sharpe Ratio</th>\n",
       "      <th>Max Drawdown</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Bagged DT/Bagged DT</th>\n",
       "      <td>28.88%</td>\n",
       "      <td>459.9%</td>\n",
       "      <td>3.61</td>\n",
       "      <td>-41.8%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bagged SVM/Bagged DT</th>\n",
       "      <td>37.65%</td>\n",
       "      <td>789.92%</td>\n",
       "      <td>4.77</td>\n",
       "      <td>-38%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ANN/Bagged DT</th>\n",
       "      <td>30.38%</td>\n",
       "      <td>480.49%</td>\n",
       "      <td>3.73</td>\n",
       "      <td>-34.36%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         PNP Cumulative Returns Sharpe Ratio Max Drawdown\n",
       "Bagged DT/Bagged DT   28.88%             459.9%         3.61       -41.8%\n",
       "Bagged SVM/Bagged DT  37.65%            789.92%         4.77         -38%\n",
       "ANN/Bagged DT         30.38%            480.49%         3.73      -34.36%"
      ]
     },
     "execution_count": 379,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "performance_risk_met_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Primary Classifier: Bagged Decision Trees\n",
    "# Secondary Classifier: Bagged Decision Trees\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Training Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.72      0.71      0.71      8452\n",
      "           1       0.52      0.53      0.53      5073\n",
      "\n",
      "    accuracy                           0.64     13525\n",
      "   macro avg       0.62      0.62      0.62     13525\n",
      "weighted avg       0.64      0.64      0.64     13525\n",
      "\n"
     ]
    }
   ],
   "source": [
    "y_pred = bagged_dt_meta.predict(new_x_train)\n",
    "print(classification_report(new_y_train, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Test Data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.68      0.49      0.57      2223\n",
      "           1       0.37      0.57      0.45      1181\n",
      "\n",
      "    accuracy                           0.52      3404\n",
      "   macro avg       0.53      0.53      0.51      3404\n",
      "weighted avg       0.58      0.52      0.53      3404\n",
      "\n"
     ]
    }
   ],
   "source": [
    "y_pred = bagged_dt_meta.predict(new_x_test)\n",
    "print(classification_report(new_y_test, y_pred))\n",
    "filt = y_pred.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Mean CV Score on Full Data Set \n",
    "(Metric: Accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [],
   "source": [
    "vert_barr = t1.loc[full_new_x.index].copy()\n",
    "scores = cvScore(bagged_dt_meta,full_new_x,full_new_y,sample_weights_meta,scoring='accuracy',\n",
    "                 t1=vert_barr,cv=10,pctEmbargo=0.01)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5477632142485878"
      ]
     },
     "execution_count": 195,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scores.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Performance and Risk Metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.2888114013898721"
      ]
     },
     "execution_count": 177,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "percentage_normalized_profit(new_y_test,new_x_test,labels_one,side,t1,bid,ask,filt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [],
   "source": [
    "bag_bag_ret = returns_series(new_x_test,side,t1,bid,ask,filt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "459.9"
      ]
     },
     "execution_count": 180,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.around(empyrical.stats.cum_returns(bag_bag_ret).iloc[-1]*100, 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.61"
      ]
     },
     "execution_count": 181,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.around(empyrical.stats.sharpe_ratio(bag_bag_ret),2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-41.8"
      ]
     },
     "execution_count": 182,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.around(empyrical.stats.max_drawdown(bag_bag_ret)*100,2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Primary Classifier: Bagged SVM\n",
    "# Secondary Classifier: Bagged Decision Trees\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Training Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.71      0.73      0.72      8388\n",
      "           1       0.54      0.52      0.53      5137\n",
      "\n",
      "    accuracy                           0.65     13525\n",
      "   macro avg       0.63      0.63      0.63     13525\n",
      "weighted avg       0.65      0.65      0.65     13525\n",
      "\n"
     ]
    }
   ],
   "source": [
    "y_pred = bagged_dt_meta.predict(new_x_train)\n",
    "print(classification_report(new_y_train, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Test Data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.74      0.53      0.61      2262\n",
      "           1       0.40      0.63      0.49      1142\n",
      "\n",
      "    accuracy                           0.56      3404\n",
      "   macro avg       0.57      0.58      0.55      3404\n",
      "weighted avg       0.62      0.56      0.57      3404\n",
      "\n"
     ]
    }
   ],
   "source": [
    "y_pred = bagged_dt_meta.predict(new_x_test)\n",
    "print(classification_report(new_y_test, y_pred))\n",
    "filt = y_pred.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Mean CV Score on Full Data Set \n",
    "(Metric: Accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "metadata": {},
   "outputs": [],
   "source": [
    "scores = cvScore(bagged_dt_meta,full_new_x,full_new_y,sample_weights_meta,scoring='accuracy',\n",
    "                 t1=vert_barr,cv=10,pctEmbargo=0.01)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5577735861891334"
      ]
     },
     "execution_count": 251,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scores.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Performance and Risk Metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.3765357110958358"
      ]
     },
     "execution_count": 252,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "percentage_normalized_profit(new_y_test,new_x_test,labels_one,side,t1,bid,ask,filt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "SVM_bag_ret = returns_series(new_x_test,side,t1,bid,ask,filt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "789.92"
      ]
     },
     "execution_count": 253,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.around(empyrical.stats.cum_returns(SVM_bag_ret).iloc[-1]*100, 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4.77"
      ]
     },
     "execution_count": 254,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.around(empyrical.stats.sharpe_ratio(SVM_bag_ret),2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.38"
      ]
     },
     "execution_count": 255,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.around(empyrical.stats.max_drawdown(SVM_bag_ret),2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Primary Classifier: MLP\n",
    "# Secondary Classifier: Bagged Decision Trees"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Training Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 363,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.70      0.72      0.71      8378\n",
      "           1       0.52      0.50      0.51      5147\n",
      "\n",
      "    accuracy                           0.64     13525\n",
      "   macro avg       0.61      0.61      0.61     13525\n",
      "weighted avg       0.63      0.64      0.64     13525\n",
      "\n"
     ]
    }
   ],
   "source": [
    "y_pred = bagged_dt_meta.predict(new_x_train)\n",
    "print(classification_report(new_y_train, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Test Data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 364,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.71      0.49      0.58      2258\n",
      "           1       0.37      0.59      0.46      1146\n",
      "\n",
      "    accuracy                           0.53      3404\n",
      "   macro avg       0.54      0.54      0.52      3404\n",
      "weighted avg       0.59      0.53      0.54      3404\n",
      "\n"
     ]
    }
   ],
   "source": [
    "y_pred = bagged_dt_meta.predict(new_x_test)\n",
    "print(classification_report(new_y_test, y_pred))\n",
    "filt = y_pred.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Mean CV Score on Full Data Set \n",
    "(Metric: Accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 374,
   "metadata": {},
   "outputs": [],
   "source": [
    "scores = cvScore(bagged_dt_meta,full_new_x,full_new_y,sample_weights_meta,scoring='accuracy',\n",
    "                 t1=vert_barr,cv=10,pctEmbargo=0.01)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 375,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5410260995853677"
      ]
     },
     "execution_count": 375,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scores.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Performance and Risk Metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 365,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.30376569780317253"
      ]
     },
     "execution_count": 365,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "percentage_normalized_profit(new_y_test,new_x_test,labels_one,side,t1,bid,ask,filt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 366,
   "metadata": {},
   "outputs": [],
   "source": [
    "MLP_bag_ret = returns_series(new_x_test,side,t1,bid,ask,filt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 367,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "480.49"
      ]
     },
     "execution_count": 367,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.around(empyrical.stats.cum_returns(MLP_bag_ret).iloc[-1]*100, 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 368,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.73"
      ]
     },
     "execution_count": 368,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.around(empyrical.stats.sharpe_ratio(MLP_bag_ret),2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 369,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-34.36"
      ]
     },
     "execution_count": 369,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.around(empyrical.stats.max_drawdown(MLP_bag_ret)*100,2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Backtest"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The SVM/Bagged Decision Tree model achieved the best performance out of the 3 models on the test dataset and will be backtested on 2011-2013 USD/JPY tick data. The exact same features as well as procedures that were used throughout this research will be used during backtesting.\n",
    "\n",
    "Backtesting will be conducted using the cross-validation method. We will use 10 folds, giving us 10 different outcomes to compare. Backtesting using cross-validation has advantages over the traditional walk-forward method as we are not just evaluating our strategy using one scenario, but instead k scenarios. The purged k-fold class mentioned previously will be used to prevent data leakage. The cumulative returns, Sharpe Ratio, and max drawdown will be calculated for each fold and displayed in a DataFrame below. The code for the backtesting function used below can be found [here](https://github.com/JackBrady/Financial-Machine-Learning-Research/blob/master/Code/Backtest.py). Please note this function is not very elegant as it essentially runs all the previous code at each iteration/fold."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "backtest_df = backtest_cv('FILE_PATH')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 412,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cum_returns</th>\n",
       "      <th>sharpe_ratio</th>\n",
       "      <th>max_drawdown</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-22.13</td>\n",
       "      <td>-1.67</td>\n",
       "      <td>-40.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-36.03</td>\n",
       "      <td>-5.80</td>\n",
       "      <td>-48.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>43.34</td>\n",
       "      <td>6.94</td>\n",
       "      <td>-4.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>139.06</td>\n",
       "      <td>4.10</td>\n",
       "      <td>-25.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>568.77</td>\n",
       "      <td>5.17</td>\n",
       "      <td>-28.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>-58.46</td>\n",
       "      <td>-4.75</td>\n",
       "      <td>-63.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>41.16</td>\n",
       "      <td>2.17</td>\n",
       "      <td>-19.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>-15.36</td>\n",
       "      <td>-1.28</td>\n",
       "      <td>-31.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>-40.68</td>\n",
       "      <td>-2.08</td>\n",
       "      <td>-69.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>-62.85</td>\n",
       "      <td>-3.54</td>\n",
       "      <td>-73.97</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    cum_returns  sharpe_ratio  max_drawdown\n",
       "1        -22.13         -1.67        -40.37\n",
       "2        -36.03         -5.80        -48.99\n",
       "3         43.34          6.94         -4.69\n",
       "4        139.06          4.10        -25.16\n",
       "5        568.77          5.17        -28.92\n",
       "6        -58.46         -4.75        -63.25\n",
       "7         41.16          2.17        -19.84\n",
       "8        -15.36         -1.28        -31.03\n",
       "9        -40.68         -2.08        -69.09\n",
       "10       -62.85         -3.54        -73.97"
      ]
     },
     "execution_count": 412,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "backtest_df"
   ]
  }
 ],
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